With increasing development of digitalized technologies, a variety of image processing apparatuses are used for scanning images of documents into electronic files. The common image processing apparatuses include for example scanners, printers, copiers, facsimile machines, document cameras, and the like.
If the document is askew placed during the process of scanning or capturing a document, or if the document is skewed during the process of transporting the document within the image processing apparatus, a skewed document image is readily obtained. For enhancing the image quality, a skewed document detection and correction technology plays an important role in the field of document analysis systems. The challenge of the skewed document detection and correction technology is to remove the non-text symbols (e.g. graphs) of the document. There are several critical approaches for removing the non-text symbols. In accordance with a first approach, adjacent pixels are combined as respective new objects, then the possible text-objects are counted and retained, and finally the remainder objects are removed. The first approach usually needs a great quantity of memory capacity. In addition, the first approach is only applicable when the text size and the image noise comply with a specified condition. In accordance with a second approach, the text portion is transformed into multiple lines by computations, then the rotating angles of the text lines are calculated, and finally a skew angle of the document is estimated according to the rotating angles. The second approach removes the non-text symbols (e.g. graphs) of the document while ignoring the document contents. According to four corners or boundaries realized from the color difference between the document and the background, the skew degree is obtained. If the color difference is not evident or the boundary is beyond the scanning range, the second approach is not applicable. The prior art technologies need a great quantity of memory capacity to store the sorted and statistic data and thus are not suitably implemented by hardware components.
Therefore, there is a need of providing improved method and device for detecting and correcting skewed image data to obviate the drawbacks encountered from the prior art.